We present the results of our new learning process applied to the CIFAR-10, Celeb-A, and ImageNet benchmark datasets, using the image sizes \(32 \times 32\) , \(64\times 64\) , and \(128\times 128\)  respectively. A sketch of the hybrid learning algorithm is given in Appendix REF . Besides standard deep learning techniques for stable optimization, our framework is fully described by the ML objective and MCMC initialization above. We use the SNGAN [1]} architectures for all models, where EBMs use the SNGAN discriminator with no normalization. The generator has batch normalization for the CIFAR-10 and Celeb-A experiments only. Table REF  displays the FID scores achieved by our model in comparison with prior methods.
